Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-executed method comprising: receiving, by a server from a first internal database, profile data associated with a first user; identifying, by the server, that the first user is not presently associated with a first employee of an insurance provider based on the profile data associated with the first user; when the first user is not presently associated with a first employee, crawling, by the server, a plurality of social networking databases to collect additional data associated with the first user; executing, by the server, an analytical model based on the first user profile data and the additional data obtained from the crawling to determine a recommended insurance product for the first user, wherein the analytical model is generated on the server; and automatically initiating, by the server, an automated communication session with a computing device operated by the first user to present the recommended insurance product to the first user by transmitting an email message containing a hyperlink directing the first user to a website displaying data associated with the recommended insurance product.
This invention relates to a computer-executed method for recommending insurance products to users based on their profile data and additional information gathered from social networking platforms. The method addresses the challenge of identifying potential insurance customers who are not currently associated with an insurance provider's employees and tailoring product recommendations to their needs. The method involves a server receiving profile data of a user from an internal database. The server determines that the user is not currently linked to any employee of the insurance provider. If the user is not associated with an employee, the server crawls multiple social networking databases to collect additional data about the user. An analytical model, generated on the server, analyzes the user's profile data and the crawled data to recommend a suitable insurance product. The server then initiates an automated communication session with the user's computing device, typically by sending an email with a hyperlink directing the user to a website displaying details about the recommended insurance product. This approach leverages external data sources to enhance personalized insurance recommendations for users who are not already engaged with the provider.
2. The method of claim 1 , wherein the first internal database is configured to store profile data associated with one or more users.
A system and method for managing user profile data involves a first internal database specifically designed to store profile information associated with multiple users. This database is part of a broader system that likely includes additional components, such as a second database or external data sources, to enhance data processing, retrieval, or analysis. The profile data may include user attributes, preferences, or behavioral information, enabling personalized services, authentication, or data-driven decision-making. The system may also incorporate mechanisms to update, validate, or secure this profile data, ensuring accuracy and privacy. By centralizing user profiles in an internal database, the system improves data accessibility, consistency, and efficiency in applications like customer relationship management, identity verification, or recommendation engines. The method may further involve integrating this profile data with other datasets or applying machine learning techniques to derive insights or automate processes. The primary challenge addressed is the fragmented or inefficient handling of user profile data across multiple systems, leading to inconsistencies, security risks, or performance bottlenecks. The solution provides a unified, scalable, and secure approach to managing user profiles, enhancing both operational efficiency and user experience.
3. The method of claim 1 , wherein the profile data includes at least one of age, geography, total insurance, gender, months as a customer, income, and life events.
This invention relates to a method for analyzing customer profile data to improve insurance services. The method addresses the challenge of effectively utilizing diverse customer information to tailor insurance offerings, enhance risk assessment, and personalize customer interactions. The core process involves collecting and processing profile data from customers, which includes demographic and behavioral attributes such as age, geography, total insurance coverage, gender, duration as a customer, income level, and significant life events. This data is then analyzed to identify patterns, trends, and correlations that inform decision-making in insurance underwriting, policy customization, and customer engagement strategies. The method may also integrate additional data sources to refine insights and adapt to changing customer needs. By leveraging this comprehensive profile data, insurers can optimize risk management, improve customer satisfaction, and develop more targeted insurance products. The approach ensures that insurance services are aligned with individual customer profiles, leading to more accurate pricing, better customer retention, and enhanced operational efficiency. The system may also include automated tools for data processing and predictive modeling to support real-time decision-making.
4. The method of claim 1 , wherein the analytical model determines recommended insurance products matching potential needs associated with one or more users using collaborative filtering technique.
This invention relates to personalized insurance product recommendations using collaborative filtering techniques. The system addresses the challenge of matching insurance products to users' potential needs by leveraging data-driven insights from user behavior and preferences. The method involves analyzing user data to identify patterns and similarities among users, then applying collaborative filtering to recommend insurance products that align with these patterns. Collaborative filtering compares user profiles, purchase histories, or other behavioral data to predict which products a user may find valuable. The analytical model processes this data to generate tailored recommendations, improving the relevance and accuracy of insurance offerings. The system may also incorporate additional user-specific factors, such as demographic information or risk profiles, to refine recommendations. By dynamically adjusting suggestions based on real-time data, the method enhances user engagement and satisfaction while optimizing insurance product distribution. This approach improves upon traditional recommendation systems by focusing on insurance-specific needs, ensuring that users receive relevant product suggestions that align with their potential risks and preferences. The method is particularly useful in digital insurance platforms where personalized recommendations drive customer acquisition and retention.
5. The method of claim 4 , wherein the collaborative filtering technique is a K-Nearest Neighbor algorithm.
This invention relates to recommendation systems that use collaborative filtering techniques to provide personalized suggestions. The problem addressed is improving the accuracy and relevance of recommendations by leveraging user behavior data. The system collects interaction data between users and items, such as ratings, clicks, or purchases, to identify patterns and similarities. A collaborative filtering technique is then applied to generate recommendations based on these patterns. Specifically, the system employs a K-Nearest Neighbor (K-NN) algorithm, which identifies the K most similar users or items to a target user or item. The algorithm calculates similarity using a distance metric, such as cosine similarity or Pearson correlation, to determine the nearest neighbors. Recommendations are generated by aggregating the preferences of these neighbors, either by averaging their ratings or selecting the most frequently interacted items. The system may also incorporate additional features, such as user demographics or item attributes, to enhance recommendation quality. The K-NN approach is particularly effective in scenarios with sparse data or cold-start problems, where traditional matrix factorization methods may struggle. The invention aims to provide more accurate and personalized recommendations by dynamically adapting to user preferences and behavior.
6. The method of claim 4 , wherein the collaborative filtering technique is UV decomposition.
A system and method for recommending items to users based on collaborative filtering techniques, specifically using UV decomposition. The method involves analyzing user-item interaction data to generate recommendations. UV decomposition is a matrix factorization technique that decomposes the user-item interaction matrix into two lower-dimensional matrices: a user matrix and an item matrix. These matrices capture latent features that represent user preferences and item characteristics. By multiplying these matrices, the system predicts missing interactions, enabling personalized recommendations. The method may also include preprocessing steps to handle sparse or noisy data, such as normalization or dimensionality reduction. The recommendations are then presented to users, improving user engagement and satisfaction. This approach is particularly useful in applications like e-commerce, streaming services, and social media, where personalized recommendations enhance user experience. The UV decomposition technique improves recommendation accuracy by effectively modeling complex user-item relationships in a scalable manner.
7. The method of claim 1 , further comprising: receiving, by the server, from a second internal database that is configured to store insurance product data and purchase history data corresponding to one or more users, insurance product data and purchase history data corresponding to the first user.
This invention relates to a system for processing insurance-related data to provide personalized recommendations or services to users. The system addresses the challenge of efficiently accessing and utilizing insurance product data and user purchase history to enhance decision-making or service delivery. The method involves a server receiving insurance product data and purchase history data for a first user from a second internal database. This database is specifically configured to store insurance product details and transaction records associated with multiple users. The received data is used to analyze the user's insurance needs, preferences, or behavior, enabling tailored recommendations, risk assessments, or other insurance-related actions. The system may integrate this data with other user-specific information to improve accuracy or relevance. The method may also include steps such as validating the received data, updating user profiles, or generating reports based on the analysis. The server may further process the data to identify trends, patterns, or opportunities for cross-selling or upselling insurance products. The system ensures secure and efficient data handling while maintaining compliance with privacy regulations. By leveraging historical purchase data and product information, the invention aims to streamline insurance operations, enhance customer experiences, and optimize business outcomes. The approach is particularly useful for insurers seeking to personalize services or improve underwriting processes.
8. The method of claim 7 , further comprising: determining, by the server, the recommended insurance product for the first user by executing the analytical model based on the first user profile data, the additional data obtained from the crawling, the insurance product data and the purchase history data associated with the first user.
This invention relates to a system for recommending insurance products to users based on personalized data analysis. The problem addressed is the inefficiency of traditional insurance recommendation systems, which often rely on generic criteria rather than tailored user profiles and real-time data. The system includes a server that collects and processes multiple data sources to generate personalized insurance recommendations. It first obtains user profile data for a first user, which includes demographic information, financial details, and preferences. The server then performs web crawling to gather additional data relevant to the user, such as market trends, competitor offerings, and external risk factors. Additionally, the system retrieves insurance product data from various providers, detailing coverage options, pricing, and eligibility criteria. The server also accesses purchase history data associated with the first user, including past insurance purchases and related transactions. Using an analytical model, the server processes this combined data to determine the most suitable insurance product for the user. The model evaluates the user's profile, the additional crawled data, the available insurance product options, and their purchase history to generate a recommendation. This approach ensures that the recommendation is highly personalized and dynamically adjusted based on the latest available information. The system aims to improve user satisfaction and conversion rates by providing more accurate and relevant insurance product suggestions.
9. The method of claim 7 , wherein the purchase history data includes at least one of an address, tax records, purchase history, and credit history of the first user.
A system and method for enhancing user authentication and security in online transactions by leveraging purchase history data. The invention addresses the problem of insecure authentication methods that rely solely on passwords or basic personal information, which are vulnerable to fraud and unauthorized access. The solution involves collecting and analyzing purchase history data associated with a user to verify their identity during transactions. This data may include an address, tax records, purchase history, and credit history, which are used to generate a unique authentication profile for the user. The system compares transaction details with the user's historical data to detect anomalies or inconsistencies, thereby improving fraud detection and authentication accuracy. By integrating multiple data points from a user's financial and transactional background, the method provides a more robust and reliable verification process compared to traditional methods. This approach enhances security while maintaining user convenience, reducing the risk of identity theft and unauthorized access. The system can be applied in e-commerce, banking, and other online services where secure authentication is critical.
10. The method of claim 1 , further comprising: training, by the server, the analytical model by executing a stochastic gradient descent algorithm.
A system and method for training an analytical model using machine learning techniques addresses the challenge of efficiently optimizing model performance in data processing applications. The invention involves a server that receives training data and a set of hyperparameters, then processes this data to train an analytical model. The model is trained to generate predictions or classifications based on input data, with the training process involving iterative adjustments to minimize prediction errors. The server evaluates the model's performance using a loss function and updates the model parameters accordingly. To enhance training efficiency, the server employs a stochastic gradient descent algorithm, which iteratively adjusts model parameters based on small, randomly selected subsets of the training data. This approach reduces computational overhead while improving convergence speed. The system may also include a client device that interacts with the server to provide input data and receive model outputs. The invention aims to optimize model training by leveraging stochastic gradient descent, ensuring faster and more resource-efficient learning compared to traditional batch gradient descent methods.
11. A system comprising: a computer readable memory having stored thereon computer executable instructions for matching an insurance product to a user; and a computer coupled to the memory, the computer executing the instructions performing steps including: receiving, by a server from a first internal database, profile data associated with a first user; identifying, by the server, that the first user is not presently associated with a first employee of an insurance provider based on the profile data associated with the first user; when the first user is not presently associated with a first employee, crawling, by the server, a plurality of social networking databases to collect additional data associated with the first user; executing, by the server, an analytical model based on the first user profile data and the additional data obtained from the crawling to determine a recommended insurance product for the first user, wherein the analytical model is generated on the server; and automatically initiating, by the server, an automated communication session with a computing device operated by the first user to offer the recommended insurance product to the first user by transmitting an email message containing a hyperlink directing the first user to a website displaying data associated with the recommended insurance product.
The system is designed for matching insurance products to users by leveraging profile data and social networking information. The problem addressed is the inefficiency in manually identifying potential customers for insurance products, particularly those not currently associated with an insurance provider's employees. The system automates the process by first retrieving profile data of a user from an internal database. If the user is not linked to an employee, the system crawls multiple social networking databases to gather additional data about the user. An analytical model, generated on the server, analyzes the combined profile and social data to recommend a suitable insurance product. The system then initiates an automated communication session, typically via email, containing a hyperlink that directs the user to a website displaying details of the recommended product. This approach enhances customer outreach by using data-driven insights to personalize insurance offers, improving engagement and conversion rates. The system operates without human intervention, streamlining the sales process for insurance providers.
12. The system of claim 11 , wherein the first internal database is configured to store profile data associated with one or more users.
This invention relates to a system for managing and utilizing user profile data within a database-driven environment. The system addresses the challenge of efficiently storing, organizing, and accessing user-specific information to enhance personalized services or operations. The system includes a first internal database specifically designed to store profile data associated with one or more users. This profile data may encompass various attributes such as user preferences, historical interactions, demographic information, or other relevant details. The database is structured to facilitate quick retrieval and processing of this data, enabling the system to deliver tailored responses or actions based on the stored profiles. Additionally, the system may incorporate mechanisms to update or modify the profile data in real-time or batch processes, ensuring the information remains current and accurate. The stored profiles can be used to support functions such as personalized recommendations, user authentication, access control, or data analytics. By centralizing user profile data in a dedicated internal database, the system improves data management efficiency, reduces redundancy, and enhances the ability to provide customized experiences or services. The invention is particularly useful in applications where user-specific information plays a critical role, such as e-commerce platforms, social networks, or enterprise systems.
13. The system of claim 11 , wherein the profile data includes at least one of age, geography, total insurance, gender, months as a customer, income, and life events.
A system for analyzing customer data in the insurance industry collects and processes profile information to assess risk and personalize services. The system gathers demographic and behavioral data, including age, geographic location, total insurance coverage, gender, customer tenure, income level, and significant life events such as marriage, home purchase, or job changes. This data is used to generate risk profiles, tailor insurance policies, and improve customer engagement. By integrating these diverse data points, the system enables insurers to make more accurate underwriting decisions, detect fraud, and offer targeted recommendations. The inclusion of life events helps predict changes in risk exposure, allowing for proactive policy adjustments. The system may also use this data to segment customers, optimize pricing, and enhance customer retention strategies. The combination of static demographic factors and dynamic life events provides a comprehensive view of each customer, improving the accuracy of risk assessments and service personalization.
14. The system of claim 11 , the analytical model determines recommended insurance products matching potential needs associated with one or more users using collaborative filtering techniques.
This invention relates to an insurance recommendation system that leverages collaborative filtering techniques to identify and suggest insurance products tailored to users' potential needs. The system analyzes user data, such as demographics, behavior, and preferences, to predict which insurance products would be most relevant. Collaborative filtering compares user profiles to identify patterns and similarities, enabling personalized recommendations. The system may also integrate additional data sources, such as historical claims or market trends, to refine its suggestions. By dynamically adapting to user behavior and preferences, the system enhances the accuracy and relevance of insurance product recommendations, improving user satisfaction and engagement. The underlying analytical model processes user interactions and feedback to continuously optimize recommendations, ensuring they align with evolving needs. This approach helps insurance providers offer more targeted and efficient services while reducing the burden on users to navigate complex product offerings. The system may be deployed in digital platforms, such as mobile apps or web portals, to provide seamless and real-time recommendations.
15. The system of claim 14 , wherein the collaborative filtering technique is a K-Nearest Neighbor algorithm.
A system for recommending items to users employs collaborative filtering to predict user preferences based on historical data. The system analyzes user-item interaction patterns, such as ratings or purchases, to identify similarities between users or items. By leveraging these similarities, the system generates personalized recommendations tailored to individual users. The collaborative filtering technique used in this system is a K-Nearest Neighbor (KNN) algorithm. The KNN algorithm identifies the K most similar users or items to a target user or item based on a distance metric, such as cosine similarity or Euclidean distance. The system then aggregates preferences from these neighbors to generate recommendations. This approach helps overcome the sparsity and cold-start problems in recommendation systems by leveraging local neighborhood information. The system may also incorporate additional features, such as user demographics or item attributes, to enhance recommendation accuracy. The KNN-based collaborative filtering technique is particularly effective in scenarios where user behavior exhibits strong local patterns, enabling the system to provide relevant and personalized recommendations.
Unknown
September 8, 2020
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.